{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "\n    Importing PyQt5 disabled by IPython, which has\n    already imported an Incompatible QT Binding: pyside2\n    ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-aeebd3d387d5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtushare\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mPyQt5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPySide2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\IPython\\external\\qt_loaders.py\u001b[0m in \u001b[0;36mload_module\u001b[1;34m(self, fullname)\u001b[0m\n\u001b[0;32m     55\u001b[0m     \u001b[0mImporting\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0ms\u001b[0m \u001b[0mdisabled\u001b[0m \u001b[0mby\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0mhas\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[0malready\u001b[0m \u001b[0mimported\u001b[0m \u001b[0man\u001b[0m \u001b[0mIncompatible\u001b[0m \u001b[0mQT\u001b[0m \u001b[0mBinding\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m     \"\"\" % (fullname, loaded_api()))\n\u001b[0m\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[0mID\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImportDenier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: \n    Importing PyQt5 disabled by IPython, which has\n    already imported an Incompatible QT Binding: pyside2\n    "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import tushare as ts\n",
    "import matplotlib.pyplot as plt\n",
    "import PyQt5\n",
    "import PySide2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ts.get_k_data('601988',start='2010-01-01',autype=\"none\")\n",
    "df.to_csv('601988中国银行.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>4.34</td>\n",
       "      <td>4.29</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.29</td>\n",
       "      <td>747071.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4.31</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.26</td>\n",
       "      <td>798832.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>4.34</td>\n",
       "      <td>4.29</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.28</td>\n",
       "      <td>675833.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4.28</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.29</td>\n",
       "      <td>4.20</td>\n",
       "      <td>587199.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4.17</td>\n",
       "      <td>532527.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open  close  high   low     volume\n",
       "date                                          \n",
       "2010-01-04  4.34   4.29  4.38  4.29  747071.31\n",
       "2010-01-05  4.31   4.34  4.36  4.26  798832.31\n",
       "2010-01-06  4.34   4.29  4.35  4.28  675833.40\n",
       "2010-01-07  4.28   4.21  4.29  4.20  587199.88\n",
       "2010-01-08  4.20   4.23  4.26  4.17  532527.77"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('601988中国银行.csv',index_col='date' ,parse_dates=['date'])[['open','close','high','low','volume']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2014','close'].plot()\n",
    "plt.plot(df.loc['2014','close'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26ff50ef668>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.loc[:,'close'],label='收盘价',linestyle='solid',c='red')\n",
    "plt.plot(df['open'],label='开盘价',ls='-.',c='blue')\n",
    "plt.xlabel('日期')\n",
    "plt.ylabel('%收盘价')\n",
    "plt.title('2014中国银行收盘价')\n",
    "plt.grid(True,axis='y')#开启网格线\n",
    "plt.legend()#开启图例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**柱状图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    244.000000\n",
       "mean       3.698730\n",
       "std        0.109817\n",
       "min        3.500000\n",
       "25%        3.620000\n",
       "50%        3.690000\n",
       "75%        3.762500\n",
       "max        4.030000\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2019','close'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[71, 109, 42, 22]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [0,0,0,0]  #统计落在各区间的天数\n",
    "for i in df.loc['2019','close']:\n",
    "    if(i>3)&(i<=3.625):\n",
    "        a[0]+=1\n",
    "    elif(i>3.625)&(i<=3.75):\n",
    "        a[1]+=1\n",
    "    elif(i>3.75)&(i<=3.875):\n",
    "        a[2]+=1\n",
    "    elif(i>3.875)&(i<=4.04):\n",
    "        a[3]+=1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([2,3,4,5],a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**直方图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([22., 27., 39., 64., 31., 24., 15., 15.,  3.,  4.]),\n",
       " array([3.5  , 3.553, 3.606, 3.659, 3.712, 3.765, 3.818, 3.871, 3.924,\n",
       "        3.977, 4.03 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.loc['2019','close'],bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 22.,  49.,  88., 152., 183., 207., 222., 237., 240., 244.]),\n",
       " array([3.5  , 3.553, 3.606, 3.659, 3.712, 3.765, 3.818, 3.871, 3.924,\n",
       "        3.977, 4.03 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.loc['2019','close'],bins=10,cumulative='stepfilled')#增量填充图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**饼图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x26ff5324ba8>,\n",
       "  <matplotlib.patches.Wedge at 0x26ff532b0b8>,\n",
       "  <matplotlib.patches.Wedge at 0x26ff532b5c0>,\n",
       "  <matplotlib.patches.Wedge at 0x26ff532ba90>],\n",
       " [Text(0.671509,0.87125,''),\n",
       "  Text(-1.09554,-0.0990063,''),\n",
       "  Text(0.491803,-0.983936,''),\n",
       "  Text(1.05616,-0.307434,'')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**箱线图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.34, 4.29, 4.38, 4.29],\n",
       "       [4.31, 4.34, 4.36, 4.26],\n",
       "       [4.34, 4.29, 4.35, 4.28],\n",
       "       ...,\n",
       "       [3.61, 3.63, 3.64, 3.61],\n",
       "       [3.64, 3.66, 3.67, 3.64],\n",
       "       [3.65, 3.63, 3.66, 3.63]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(df.iloc[:,:4])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x26ff6582898>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff66f5898>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff651bac8>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff545f160>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff65db7f0>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff65dbd30>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6521f28>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6524860>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x26ff65885f8>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6594400>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6578828>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff4dfd550>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff626ae80>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff65e2be0>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6474ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff64745c0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x26ff56b8978>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff61a1668>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff4cc75c0>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff66b15c0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x26ff6594cc0>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff650be48>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff6441128>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff665bba8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x26ff6579048>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff51979e8>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff663cef0>,\n",
       "  <matplotlib.lines.Line2D at 0x26ff4cd65c0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data,labels=('open','close','high','low'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**figure，axes对象与多图绘制**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26ff76599b0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()#先创建一块画布\n",
    "ax1 = fig.add_axes([0.1,0.1,0.3,0.3])\n",
    "ax2 = fig.add_axes([0.5,0.5,0.4,0.4])#列表前两个元素指定区域的左下角坐标，后两个元素指定x，y轴的长度\n",
    "ax1.plot(df.loc['2019-12','close'][:10])\n",
    "ax1.set_title(\"12月前十个交易日收盘价\")\n",
    "ax1.set_xlabel(\"日期\")\n",
    "ax1.set_xticklabels(df.loc['2019-12','close'].index[:10],rotation=15)\n",
    "ax1.set_ylabel(\"收盘价\")\n",
    "ax1.set_ylim(3.5,3.7)\n",
    "ax2.plot(df.loc['2019-12','open'][:10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-12-02    3.64\n",
       "2019-12-03    3.62\n",
       "2019-12-04    3.60\n",
       "2019-12-05    3.61\n",
       "2019-12-06    3.62\n",
       "2019-12-09    3.63\n",
       "2019-12-10    3.61\n",
       "2019-12-11    3.62\n",
       "2019-12-12    3.62\n",
       "2019-12-13    3.62\n",
       "Name: open, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2019-12','close'][:10]\n",
    "df.loc['2019-12','open'][:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(221)#22表示2*2个子图\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 113 artists>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close19 = df.loc['2019','close']\n",
    "open19 = df.loc['2019','open']\n",
    "volume19 = df.loc['2019','volume']\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "ax2 = plt.subplot(212)\n",
    "ax1.plot(close19,'--*',label='close')\n",
    "ax1.plot(open19,'-+',label='open')\n",
    "ax1.legend()\n",
    "\n",
    "left1 = volume19.index[close19>=open19]\n",
    "hight1 = volume19[left1]\n",
    "left2 = volume19.index[close19<open19]\n",
    "hight2 = volume19[left2]\n",
    "ax2.bar(left1,hight1,color='r')\n",
    "ax2.bar(left2,hight2,color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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